US9052969B2 - Programming model for transparent parallelization of combinatorial optimization - Google Patents
Programming model for transparent parallelization of combinatorial optimization Download PDFInfo
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- US9052969B2 US9052969B2 US13/794,029 US201313794029A US9052969B2 US 9052969 B2 US9052969 B2 US 9052969B2 US 201313794029 A US201313794029 A US 201313794029A US 9052969 B2 US9052969 B2 US 9052969B2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/54—Interprogram communication
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2453—Query optimisation
- G06F16/24534—Query rewriting; Transformation
- G06F16/24542—Plan optimisation
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- G06F17/30463—
Definitions
- the network interface 116 allows processor 102 to be coupled to another computer, computer network, or telecommunications network using a network connection as shown.
- the processor 102 can receive information, for example data objects or program instructions, from another network, or output information to another network in the course of performing method/process steps.
- Information often represented as a sequence of instructions to be executed on a processor, can be received from and outputted to another network.
- An interface card or similar device and appropriate software implemented by, for example executed/performed on, processor 102 can be used to connect the computer system 100 to an external network and transfer data according to standard protocols.
- auxiliary I/O device interface can be used in conjunction with computer system 100 .
- the auxiliary I/O device interface can include general and customized interfaces that allow the processor 102 to send and, more typically, receive data from other devices such as microphones, touch-sensitive displays, transducer card readers, tape readers, voice or handwriting recognizers, biometrics readers, cameras, portable mass storage devices, and other computers.
- FIG. 3 represents a similar query plan tree as an abstraction with table A, table B, and table C and the Join operator.
- the query plan tree can be divided into subgroups of problems, represented by the numbers one through five.
- Execution Order Beginning the execution of some subtasks may depend on completing the execution of other subtasks. For example, an Implement Group subtask does not start execution until an Explore Group subtask, on the same group, has already completed execution. Thus state information needs to be passed among different subtasks.
- Modularity The disclosed programming model for transparent parallelization of combinatorial optimization provides a fine-grained decomposition of the logic of each optimization subtask as a set of smaller actions with predefined transitions provides a great potential for extensibility and better modular design of the query optimization task.
- the disclosed programming model for transparent parallelization of combinatorial optimization can be seamlessly integrated into modern transformation-based query optimizers and other dynamic programming problems.
- Many query optimizers build on the concepts of memoization and functional decomposition for scalability and extensibility. It can be used by new query optimizers for targeting massive parallelization and scalability objectives.
Abstract
Description
-
- 1. enables effective encoding of optimization steps;
- 2. enables a single-thread view on the problem, that is, it manages parallelism transparently; and
- 3. manages state of an optimization step, in part by encoding, preserving, and restoring the state of an optimization step.
SELECT * |
FROM FLIGHTS F |
WHERE F.FROM = ‘SFO’ | ||
AND F.TO = ‘BOI’ | ||
to look up the airline flights from San Francisco, Calif. to Boise, Id. There may be 100,000 flights in table FLIGHTS, of which 600 originate from SFO, and also of which 10 terminate in BOI.
instead of:
GROUP | GROUP EXPRESSION | ||
5 (Root Group) | Join (3, 4) | ||
4 | | ||
3 | Join (1, 2) | ||
2 | B | ||
1 | A | ||
For example,
GROUP | GROUP EXPRESSION | ||
5 (Root Group) | Join (3, 4), Join (4, 3) | ||
4 | |
||
3 | Join (1, 2), Join (2, 1) | ||
2 | B | ||
1 | A | ||
GROUP | GROUP EXPRESSION | ||
6 (Root Group) | Join (3, 4), Join (4, 3), Join (2, 5), Join (5, 2) | ||
5 | Join (1, 4), Join (4, 1) | ||
4 | |
||
3 | Join (1, 2), Join (2, 1) | ||
2 | B | ||
1 | A | ||
Join (1, 2), Join (2, 1), SMJ (1, 2), HJ (1, 2), NLJ (1, 2), SMJ (2, 1), HJ |
(2, 1), NLJ (2, 1) . . . |
A, TableScan (A), . . . | ||
-
- Optimize Group. This subtask takes as inputs a group, and an optimization context, which is a set of required physical properties such as the sort order of query output tuples. The subtask returns the most efficient execution plan that implements the group under the given optimization context. Optimizing a group entails implementing the group, as described in the next optimization subtask.
- Implement Group. This subtask creates implementations of logical group expressions in a given group. Implementing a group entails first exploring the group, and then iteratively implementing group expressions, as described in the next optimization subtasks.
- Explore Group. This subtask creates logically equivalent expressions of the logical group expressions in a given group. Exploring a group entails exploring group expressions, as described in the next optimization subtasks.
- Explore Group Expression. This subtask creates logically equivalent expressions of a given logical group expression. Exploring a group expression entails transforming group expression into a set of equivalent logical expressions, as described in the next optimization subtask.
- Transform Group Expression. This subtask applies a given transformation rule to a logical group expression. A rule is specified using a pattern tree, which is used to match operator trees in the Memo, and a result tree, which describes how output looks like after applying the transformation to the pattern tree. For example, a join commutativity rule has a pattern tree ‘Join(*1,*2)’, where * denotes an arbitrary operator, and a result tree ‘Join(*2,*1)’. A transformation rule can be either an exploration rule, where both pattern tree and result tree are composed of logical operators, or an implementation rule, where only pattern tree is composed of logical operators whereas result tree is composed of physical operators.
- Implement Group Expression. This subtask creates implementation alternatives of a given logical group expression. Implementing a group expression entails first implementing the child groups of the group expression, and then transforming group expression into possible physical implementation alternatives, as described in the next optimization subtask.
- Explore Group. This subtask creates logically equivalent expressions of the logical group expressions in a given group. Exploring a group entails exploring group expressions, as described in the next optimization subtasks.
- Implement Group. This subtask creates implementations of logical group expressions in a given group. Implementing a group entails first exploring the group, and then iteratively implementing group expressions, as described in the next optimization subtasks.
- Optimize Group. This subtask takes as inputs a group, and an optimization context, which is a set of required physical properties such as the sort order of query output tuples. The subtask returns the most efficient execution plan that implements the group under the given optimization context. Optimizing a group entails implementing the group, as described in the next optimization subtask.
-
- s1 suspends its execution waiting for s2 to terminate;
- if s2 has generated new group expressions, s1 needs to be resumed to trigger the exploration of these new expressions. Then, s1 goes back to suspension state; and
- if s2 has not generated any new expressions, s1 is resumed, and then it can immediately terminate.
-
- Initializing (502): In this state, optimization subtask is initialized and allocates the required resources.
- Exploring Child Groups (504): In this state, the child groups of group expression are iterated upon, triggering the execution of an Explore Group subtask on each child group.
- Exploring Self (506): In this state, the exploration rules applicable to the group expression are iterated upon, and trigger the execution of a Transform Group Expression subtask for each exploration rule.
- Finalizing (508): In this state, the resources used by the subtask are cleaned up, and set the state of group expression to ‘explored’ to prevent redoing the same subtask later.
- Complete (510): This is the subtask's terminal state.
-
- i. Retrieve the subtask's current state;
- ii. Load and execute the action associated with the current state; and
- iii. Input the event returned by the action to the subtask's state machine, and examine the new state of the state machine. If the state machine is in terminal state, then end subtask execution. If state machine is in a new state, go back to step (i). Otherwise, state machine is still in the same state, and subtask is suspended until dependent subtasks are complete.
-
- i. Define a state machine for each optimization subtask.
- ii. For each optimization subtask, create a separate function that includes the logic pertinent to each subtask state.
- iii. Run the state machine of the main optimization subtask, for example Optimize Group on the root group for query optimization.
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US20150032722A1 (en) * | 2013-03-15 | 2015-01-29 | Teradata Corporation | Optimization of database queries for database systems and environments |
US9659057B2 (en) | 2013-04-15 | 2017-05-23 | Vmware, Inc. | Fault tolerant distributed query processing using query operator motion |
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CN104424326B (en) * | 2013-09-09 | 2018-06-15 | 华为技术有限公司 | A kind of data processing method and device |
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US10747815B2 (en) * | 2017-05-11 | 2020-08-18 | Open Text Sa Ulc | System and method for searching chains of regions and associated search operators |
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